38 research outputs found
An analysis of feature relevance in the classification of astronomical transients with machine learning methods
The exploitation of present and future synoptic (multi-band and multi-epoch)
surveys requires an extensive use of automatic methods for data processing and
data interpretation. In this work, using data extracted from the Catalina Real
Time Transient Survey (CRTS), we investigate the classification performance of
some well tested methods: Random Forest, MLPQNA (Multi Layer Perceptron with
Quasi Newton Algorithm) and K-Nearest Neighbors, paying special attention to
the feature selection phase. In order to do so, several classification
experiments were performed. Namely: identification of cataclysmic variables,
separation between galactic and extra-galactic objects and identification of
supernovae.Comment: Accepted by MNRAS, 11 figures, 18 page
Challenges in the automated classification of variable stars in large databases
With ever-increasing numbers of astrophysical transient surveys, new facilities and archives of astronomical time series, time domain astronomy is emerging as a mainstream discipline. However, the sheer volume of data alone - hundreds of observations for hundreds of millions of sources – necessitates advanced statistical and machine learning methodologies for scientific discovery: characterization, categorization, and classification. Whilst these techniques are slowly entering the astronomer’s toolkit, their application to astronomical problems is not without its issues. In this paper, we will review some of the challenges posed by trying to identify variable stars in large data collections, including appropriate feature representations, dealing with uncertainties, establishing ground truths, and simple discrete classes
Neural Networks and Photometric Redshifts
We present a neural network based approach to the determination of
photometric redshift. The method was tested on the Sloan Digital Sky Survey
Early Data Release (SDSS-EDR) reaching an accuracy comparable and, in some
cases, better than SED template fitting techniques. Different neural networks
architecture have been tested and the combination of a Multi Layer Perceptron
with 1 hidden layer (22 neurons) operated in a Bayesian framework, with a Self
Organizing Map used to estimate the accuracy of the results, turned out to be
the most effective. In the best experiment, the implemented network reached an
accuracy of 0.020 (interquartile error) in the range 0<zphot<0.3, and of 0.022
in the range 0<zphot<0.5.Comment: 9 pages, 5 figures, 3 table
Connecting the time domain community with the Virtual Astronomical Observatory
The time domain has been identified as one of the most important areas of
astronomical research for the next decade. The Virtual Observatory is in the
vanguard with dedicated tools and services that enable and facilitate the
discovery, dissemination and analysis of time domain data. These range in scope
from rapid notifications of time-critical astronomical transients to annotating
long-term variables with the latest modeling results. In this paper, we will
review the prior art in these areas and focus on the capabilities that the VAO
is bringing to bear in support of time domain science. In particular, we will
focus on the issues involved with the heterogeneous collections of (ancillary)
data associated with astronomical transients, and the time series
characterization and classification tools required by the next generation of
sky surveys, such as LSST and SKA.Comment: Submitted to Proceedings of SPIE Observatory Operations: Strategies,
Processes and Systems IV, Amsterdam, 2012 July 2-
A systematic search for close supermassive black hole binaries in the Catalina Real-Time Transient Survey
Hierarchical assembly models predict a population of supermassive black hole
(SMBH) binaries. These are not resolvable by direct imaging but may be
detectable via periodic variability (or nanohertz frequency gravitational
waves). Following our detection of a 5.2 year periodic signal in the quasar PG
1302-102 (Graham et al. 2015), we present a novel analysis of the optical
variability of 243,500 known spectroscopically confirmed quasars using data
from the Catalina Real-time Transient Survey (CRTS) to look for close (< 0.1
pc) SMBH systems. Looking for a strong Keplerian periodic signal with at least
1.5 cycles over a baseline of nine years, we find a sample of 111 candidate
objects. This is in conservative agreement with theoretical predictions from
models of binary SMBH populations. Simulated data sets, assuming stochastic
variability, also produce no equivalent candidates implying a low likelihood of
spurious detections. The periodicity seen is likely attributable to either jet
precession, warped accretion disks or periodic accretion associated with a
close SMBH binary system. We also consider how other SMBH binary candidates in
the literature appear in CRTS data and show that none of these are equivalent
to the identified objects. Finally, the distribution of objects found is
consistent with that expected from a gravitational wave-driven population. This
implies that circumbinary gas is present at small orbital radii and is being
perturbed by the black holes. None of the sources is expected to merge within
at least the next century. This study opens a new unique window to study a
population of close SMBH binaries that must exist according to our current
understanding of galaxy and SMBH evolution.Comment: 29 pages, 10 figures, accepted for publication in MNRAS - this
version contains extended table and figur
Machine-assisted discovery of relationships in astronomy
High-volume feature-rich data sets are becoming the bread-and-butter of 21st century astronomy but present significant challenges to scientific discovery. In particular, identifying scientifically significant relationships between sets of parameters is non-trivial. Similar problems in biological and geosciences have led to the development of systems which can explore large parameter spaces and identify potentially interesting sets of associations. In this paper, we describe the application of automated discovery systems of relationships to astronomical data sets, focusing on an evolutionary programming technique and an information-theory technique. We demonstrate their use with classical astronomical relationships – the Hertzsprung–Russell diagram and the Fundamental Plane of elliptical galaxies. We also show how they work with the issue of binary classification which is relevant to the next generation of large synoptic sky surveys, such as the Large Synoptic Survey Telescope (LSST). We find that comparable results to more familiar techniques, such as decision trees, are achievable. Finally, we consider the reality of the relationships discovered and how this can be used for feature selection and extraction
A possible close supermassive black-hole binary in a quasar with optical periodicity
Quasars have long been known to be variable sources at all wavelengths. Their
optical variability is stochastic, can be due to a variety of physical
mechanisms, and is well-described statistically in terms of a damped random
walk model. The recent availability of large collections of astronomical time
series of flux measurements (light curves) offers new data sets for a
systematic exploration of quasar variability. Here we report on the detection
of a strong, smooth periodic signal in the optical variability of the quasar PG
1302-102 with a mean observed period of 1,884 88 days. It was identified
in a search for periodic variability in a data set of light curves for 247,000
known, spectroscopically confirmed quasars with a temporal baseline of
years. While the interpretation of this phenomenon is still uncertain, the most
plausible mechanisms involve a binary system of two supermassive black holes
with a subparsec separation. Such systems are an expected consequence of galaxy
mergers and can provide important constraints on models of galaxy formation and
evolution.Comment: 19 pages, 6 figures. Published online by Nature on 7 January 201
Extreme Variability in a Broad Absorption Line Quasar
CRTS J084133.15+200525.8 is an optically bright quasar at z=2.345 that has
shown extreme spectral variability over the past decade. Photometrically, the
source had a visual magnitude of V~17.3 between 2002 and 2008. Then, over the
following five years, the source slowly brightened by approximately one
magnitude, to V~16.2. Only ~1 in 10,000 quasars show such extreme variability,
as quantified by the extreme parameters derived for this quasar assuming a
damped random walk model. A combination of archival and newly acquired spectra
reveal the source to be an iron low-ionization broad absorption line (FeLoBAL)
quasar with extreme changes in its absorption spectrum. Some absorption
features completely disappear over the 9 years of optical spectra, while other
features remain essentially unchanged. We report the first definitive redshift
for this source, based on the detection of broad H-alpha in a Keck/MOSFIRE
spectrum. Absorption systems separated by several 1000 km/s in velocity show
coordinated weakening in the depths of their troughs as the continuum flux
increases. We interpret the broad absorption line variability to be due to
changes in photoionization, rather than due to motion of material along our
line of sight. This source highlights one sort of rare transition object that
astronomy will now be finding through dedicated time-domain surveys.Comment: 6 pages, 4 figures; accepted for publication in Ap